Random Forest Models To Predict Aqueous Solubility
David S. Palmer, Noel M. O’Boyle,†Robert C. Glen, and John B. O. Mitchell*
Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge,
Lensfield Road, Cambridge CB2 1EW, United Kingdom
Received May 5, 2006
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM),
and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous
solubility, based on experimental data for 988 organic molecules. The Random Forest regression model
predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods
for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of
predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an
external test set of 330 molecules that are solid at 25 °C gave an r2) 0.89 and RMSE ) 0.69 log S units.
For a standard data set selected from the literature, the model performed well with respect to other documented
methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by
molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression
The majority of drugs that enter development never reach
the marketplace. The cost of research and development is
such that by 2000 the annual expenditure of U.S. pharma-
ceutical companies had reached 26 billion dollars. Of the
costs incurred in research and development, approximately
75% have been attributed to failures of potential drug
molecules.1Therefore there has been much interest in
industry in the development of in silico tools to guide drug
The factors which influence the transit of a drug in the
body are termed the pharmacokinetic phase and are com-
monly described by the acronym ADMET (Absorption,
Distribution, Metabolism, Excretion, and Toxicity). In silico
screens have been developed for many of the ADMET
properties. Many of these are based upon Quantitative-
Structure-Property-Relationships (QSPR), which attempt to
relate the physical property of interest to descriptors calcu-
lable from a computer representation of the molecule. By
their nature, most of these in silico screens are statistical
models whose validity is dependent upon the data from which
they are derived and the methods used and assumptions made
during their construction. Therefore they are frequently
improved as new data and methods become available.
In this study, we develop models to predict aqueous
solubility. The solubility of a potential drug candidate is an
important determinant of its bioavailability. From the solubil-
ity the rate of dissolution may also be calculated by models
such as those of Hamlin et al.2Solubility is defined as the
concentration of solute in a saturated solution under equi-
librium conditions. However, the solubility of an organic
molecule in aqueous media may depend on temperature, pH,
counterions, impurities, and the polymorphic form of the
solute. We develop a QSPR model for the prediction of
(thermodynamic) aqueous solubility at 25 °C.
Of the QSPR models documented in the literature, many
are derived from data sets that contain both molecules that
are solids and liquids at room temperature. The process of
solvation of a crystalline organic molecule can be decom-
posed into three steps: (1) breakdown of the crystal lattice;
(2) cavity formation in the solvent; and (3) solvation of the
molecule. However, for the dissolution of a liquid the first
step corresponds to overcoming the liquid-liquid interac-
tions, whereas for dissolution of a crystalline solid this
corresponds to overcoming the lattice interactions.3Therefore
in this study we have investigated the prediction of solubility
for a data set of molecules which are all solid at room
temperature. The use of a data set which contains solids
makes it necessary in principle to consider polymorphic form,
as many molecules can crystallize to form different crystal
structures. For organic molecules, these polymorphs are
normally found to have similar lattice energies, rarely
differing by more than 10 kJ mol-1. The polymorphic form
is not considered explicitly in this work because no informa-
tion about crystal form was available for the molecules in
the data set. It is hoped that the effect of lattice energy on
solubility may be accounted for indirectly during model
building. This idea might be supported by the work of
Ouvrard and Mitchell,4who demonstrated that it is possible
to predict the lattice energy from simple molecular descrip-
tors. Examples from the literature where the intermolecular
interaction energy in the solid phase has been explicitly
considered in modeling solubility are the work of Yalkowsky5
and of Abraham.6
The methods employed in deriving solubility models can
be grouped by the molecular descriptors selected, the method
of regression, and the diversity and constitution of the data
set. The last property is difficult to assess because many
* Corresponding author phone: +44-1223-762983; fax: +44-1223-
763076; e-mail: firstname.lastname@example.org.
†Current address: Cambridge Crystallographic Data Centre, 12 Union
Road, Cambridge CB2 1EZ, U.K.
10.1021/ci060164k CCC: $37.00 © xxxx American Chemical Society
PAGE EST: 9
Published on Web 12/02/2006
QSPR papers have provided little information about the data
set they employ. One exception is the work of Bergstrom et
al.,7who used the Chemography method8to assess the
diversity of their data set. The methods of regression used
for solubility modeling can be separated into linear and
nonlinear methods. The question of which is more appropri-
ate has been addressed, and for a single data set linear
methods were selected.9However, the trend in the literature
is for nonlinear methods, especially Neural Networks, to give
better validation statistics. Lind et al. demonstrated the use
of a Support Vector Machine with a Tanimoto similarity
kernel.10The problem with both Support Vector Machines
and Neural Networks is that they are often ‘black boxes’; it
can be difficult to interpret the importance of individual
descriptors to the model. In addition, both methods can be
time-consuming to train, which may be a problem as a
common occurrence in QSPR is the need to retrain the model
as new or more diverse data become available. Finally, the
models can be separated into those that employ either 2D
descriptors or both 2D and 3D descriptors. Perhaps surpris-
ingly, the best models based on 2D information often
outperform those that also incorporate 3D descriptors. It is
possible that the bulk properties encoded by 2D descriptors
are more important to solubility. Another possibility is that
3D descriptors may suffer from inaccuracies related to the
selection of a single 3D molecular conformer. The most
significant descriptor in solubility prediction is usually the
calculated logarithm of the octanol-water partition coef-
ficient, which is calculated from a 2D representation of a
molecule. Three recent review articles have discussed the
prediction of solubility from structure.11-13
In this paper, we develop Random Forest models for
aqueous solubility from a large data set of molecules that
are solid at room temperature. We also investigate models
developed with Partial-Least-Squares, a Support Vector
Machine with a radial-basis-function kernel, and Artificial
Neural Networks. Sheridan et al. have demonstrated that
dissimilarity between training and test sets correlates with
experimental error.14To assess the relative diversities of our
training and test sets, we compare these to the chemical space
occupied by the molecules in the MDL Drug Data Report
Random Forest. Random Forest is a method for clas-
sification and regression which was introduced by Breiman
and Cutler.16Recent studies have suggested that Random
Forest offers features which make it very attractive for QSPR
studies.17These include relatively high accuracy of predic-
tion, built-in descriptor selection, and a method for assessing
the importance of each descriptor to the model. The theory
of Random Forest regression is discussed in the papers of
Svetnik et al.17and on the Web site of Breiman and Cutler.18
The method is based upon an ensemble of decision trees,
from which the prediction of a continuous variable is
provided as the average of the predictions of all trees.
In RF regression, an ensemble of regression trees is grown
from separate bootstrap samples of the training data using
the CART algorithm.19The branches in each tree continue
to be subdivided while the minimum number of observations
in each leaf is greater than a predetermined value. Unlike
regression trees, the branches are not pruned back. Further-
more, the descriptor selected for branch splitting at any fork
in any tree is not selected from the full set of possible
descriptors but from a randomly selected subset of prede-
termined size. There are three possible training parameters
for Random Forest: ntree - the number of trees in the
Forest; mtry - the number of different descriptors tried at
each split; and nodesize - the minimum node size below
which leaves are not further subdivided.
The bootstrap sample used during tree growth is a random
selection with replacement from the molecules in the data
set. The molecules that are not used for tree growth are
termed the ‘out-of-bag sample’. Each tree provides a
prediction for its out-of-bag sample, and the average of these
results for all trees provides an in situ cross-validation called
the ‘out-of-bag validation’.
Random Forest includes a method for assessing the
importance of descriptors to the model. When each descriptor
is replaced in turn by random noise, then the resulting
deterioration in model quality is a measure of descriptor
importance. The deterioration in model quality can be
assessed by the change in mean-square-error for the out-of-
Software for Random Forest regression is available from
the Web site of Breiman and Cutler or as part of the Random
Forest package in the statistical computing environment, R.20
Support Vector Machines. A Support Vector Machine
is a kernel-based method for classification, regression, and
function approximation. A thorough discussion of the theory
of Support Vector Machines is provided by Cristianini and
Shawe-Taylor.21Lind et al. have previously described the
use of a Support Vector Machine with a Tanimoto kernel
for the prediction of aqueous solubility.10Here we discuss
the use of a Support Vector Machine with a radial basis
Ant Colony Optimization Algorithm. An Ant Colony
Optimization (ACO) algorithm is employed to select a subset
of variables for regression by Partial-Least-Squares, Support
Vector Machines, and Artificial Neural Networks. Variable
selection in QSPR modeling is the problem of selecting an
optimum subset from a large number of descriptors so that
the best model is formed. The brute force method of selection
would be to build every possible model and select the best
one. As this is not feasible, it is common practice to use an
algorithm to guide the search. In this paper, an Ant Colony
Optimization algorithm is used because recent evidence has
suggested that they may converge on the optimum solution
in a shorter period of time than genetic algorithms.22
Ant Colony Optimization algorithms are methods for
solving discrete optimization problems which were intro-
duced by Dorigo.23The algorithm is modeled upon the
behavior of real ant colonies whose members exhibit
stigmergysa form of indirect communication mediated by
modifications of the environment. A common example of
stigmergy is the sight of ants walking in lines. During the
search for food, each ant lays a trail of pheromone between
nest and food source that is later followed by other ants with
a probability dependent upon the amount of pheromone laid
down. As each ant lays additional pheromone upon the trail,
the process becomes autocatalytic so that the Ant Colony
exhibits self-organizing behavior.
BJ. Chem. Inf. Model.PALMER ET AL.
We implemented an Ant Colony Optimization algorithm
in R, based on the paper of Shen et al.22It was observed
that for the fitness function of Shen et al. the term which
penalizes the inclusion of extra descriptors swamps the term
which measures the quality of the fit. Therefore their
algorithm tends to select a very small model. For this reason,
the Shen fitness function was replaced by the RMSE for 10-
MATERIALS AND METHODS
Data Set. The data set was selected from the Handbook
of Aqueous Solubility24and two sources from the litera-
ture.25,26Molecules were selected on the basis that they were
organic compounds, structurally diverse and solid at room
temperature. For some of the molecules in the Handbook of
Aqueous Solubility, more than one experimental measure-
ment is provided. In these cases, the molecule was ignored
if the reported values were considered to be inconsistent.
Otherwise the molecule was accepted, and a mean value for
the molar aqueous solubility was taken. The data set was
randomly partitioned into a training set of 658 molecules
and a test set of 330 molecules. The range of log molar
solubility values (moles of solute per liter) in the training
set was from -10.41 to 1.58 with a mean value of -3.42
and a standard deviation of 2.13. The distribution of
molecular weights was from 59.1 to 511.6 amu, with a mean
of 244.0 and a standard deviation of 90.1. For the test set,
the range of log molar solubility values was from -9.16 to
1.09 with a mean value of -3.11 and a standard deviation
of 2.06. The distribution of molecular weights was from 60.1
to 588.6 amu with a mean of 235.0 and a standard deviation
of 89.9. A diversity analysis is provided for the training and
test sets at the end of the Experimental Section.
Statistical Testing. For each model that was tested, three
statistics are reported; these are the squared correlation
coefficient (r2), the Root-Mean-Square-Error (RMSE), and
the bias, and they are defined in eqs 1-3. A parenthesis
nomenclature is adopted to indicate whether the relevant
statistic refers to the training set (tr), the 10-fold cross-
validation inside the training set (CV), the Random Forest
out-of-bag validation inside the training set (oob), or the
prediction of the test set (te).
All models were derived by regression against the
molecules in the training set. The test set was not used in
model building. The 10-fold cross-validation results were
selected as the standard by which to compare Random Forest
results with other models. The out-of-bag validation is
convenient for Random Forest models, due to the bootstrap
method of data selection which they utilize. It is not
appropriate for our PLS, SVM, or ANN models, which are
not based on bootstrap sampling. Although, in principle, a
similar data selection and cross-validation technique could
be combined with PLS, SVM, or ANN models, this is very
rarely done in practice.
Structure Optimization and Descriptors. The data set
was converted from SMILES to 3D structures using CON-
CORD in SYBYL6.9.27All further calculations were carried
out in the Molecular Operating Environment (MOE).28First,
explicit hydrogen atoms were added using the MOE Wash
function. The structures were then optimized using the
MMFF94 force field before the force field partial charges
were replaced by PM3 partial charges. All descriptors were
calculated from this single conformer.
The program MOE supports the calculation of more than
200 molecular descriptors (excluding fingerprints), of which
126 2D descriptors and 36 3D descriptors were calculated
here. This corresponds to all descriptors that do not require
semiempirical Quantum Mechanical calculations or an
external alignment step. The 2D descriptors included calcu-
lated physical properties (logP, molar refractivity), charged-
surface properties (from Gasteiger-Marsili PEOE charge
distributions on VDW surfaces), constitutional descriptors
(counts of atoms and functional groups), connectivity and
topological indices (including the chi, Kier-Hall, Kappa,
Wiener, and Balaban indices), and pharmacophore feature
counts (numbers of hydrogen bond donors and acceptors).
The 3D descriptors included energy terms (total potential
energy and contributions of angle bend, electrostatic, out-
of-plane, and solvation terms to the molecular mechanics
force-field energy), molecular shape descriptors (water-
accessible surface areas), volume descriptors, and surface
area descriptors. The models were first selected from 2D
descriptors only and then from a combined set of both 2D
and 3D descriptors.
Random Forest. Random Forests were trained using the
randomForest29library in the statistical computing environ-
ment, R. The Random Forest was trained upon all the
calculated 2D descriptors. Optimization of training param-
eters was performed using R scripts which iteratively
changed each parameter one-by-one and regenerated the
regression model. The optimum value of each parameter was
selected from the following ranges: mtry - from 1 to 126;
ntree - from 1 to 5000; nodesize - from 1 to 50. To assess
the quality of the model, the fit to the training data and the
out-of-bag validation statistics were considered. The fit to
the training data was high in all models with r2∼ 0.98 and
RMSE(tr) ∼ 0.30 log S units, which demonstrates the ability
of the model to learn the information in the training set.
However this is not an indication of predictive ability, and
so the out-of-bag cross-validation results were used to guide
the model building process. The Random Forest was
observed to be reasonably insensitive to training parameters,
so that variation of mtry between 40 and 126, of ntree from
250 upward, and of nodesize in the region 5-10 had little
effect on the cross-validation results. When mtry becomes
very small, not enough descriptors are considered at each
split, and hence the predictive quality of each tree decreases.
The exact value of mtry below which a decrease in predictive
error is observed will depend on the number and relative
importance of descriptors present in the data set. As the
nodesize is increased above the optimum value, the size and
range of solubilities in the terminal nodes of the trees
r2) 1 -∑
RANDOM FOREST MODELS
J. Chem. Inf. Model.C
increases and the predictive accuracy decreases. When the
value of ntree is decreased too far, the results deteriorate
significantly; if ntree reaches one, the Random Forest
becomes a single unpruned Regression Tree. If the number
of trees in the forest is increased above the optimum, there
is a general increase in computational expense, but the results
do not improve significantly. For this data set an optimum
value is reached at 500 trees, though a somewhat smaller
value around 250 carries only a very minor cost in accuracy
and would be more appropriate for work with larger data
sets where CPU time is a limiting factor. Based upon this
analysis the parameters were selected with values of mtry
) 42, nodesize ) 5, and ntree )500.
Descriptor Selection for Random Forest. Random Forest
incorporates a method for descriptor selection. However the
selection process means that irrelevant descriptors may be
incorporated into a very small proportion of the trees
generated (as a consequence of the sampling procedure) or
may be included in the Forest but without being used in the
final model. Therefore efforts were made to remove irrelevant
descriptors. Descriptor importance was assessed by replacing
each descriptor in turn by random noise and observing the
increase in the Mean-Square-Error (MSE) for the out-of-
bag validation. When the descriptors were then sorted by
relative importance and plotted against the increase in MSE,
a graph similar in appearance to a scree plot was obtained
(Figure 2). Random Forests were retrained upon smaller
subsets of descriptors by pruning away the least important
Partial-Least Squares, Support Vector Machines, and
Artificial Neural Networks. The most important descriptors
for the Random Forest model may not be the best subset for
other regression models (which will be discussed). Indeed
it was found that better results were obtained for all three
methods when they were trained upon a subset of descriptors
selected by an Ant Colony Optimization algorithm and
Partial-Least-Squares regression. Linear-PLS is widely used
as the regression method to select descriptors for nonlinear
predictors.9,30It may, however, miss descriptors which have
strongly nonlinear relationships with the variable to be
predicted. Some attempts were made to use the Ant Colony
Algorithm for direct selection of descriptors using a SVM
and an ANN as the regression method. However, as no
improvement was observed, these results are not reported.
The ACO/PLS procedure is similar to the widely used GA/
PLS method.30,31Recent studies suggest that an ACO-based
method may converge on optimum solutions in less time.22
The ACO/PLS selection was made from all 126 2D
descriptors with 50 ants, a pheromone evaporation parameter
of 0.7, and an RMSE(CV) fitness function. The selected
descriptors were then used as input for Partial-Least-Squares
regression and two different machine learning methods (i) a
Support Vector Machine for epsilon regression and (ii) a
multilayer perceptron feed-forward backward-propagation
neural network. The method for descriptor selection for all
models is summarized in Figure 1.
The SVM training parameters (epsilon, cost, gamma) were
selected by a systematic search, using the tune function in
the e1071 library in R, so as to minimize the mean-square-
error of prediction for an internal hold-out test set. The search
was carried out in the following ranges: epsilon - 0.01 to
1; cost - 1 to 20; and gamma - 0.05 to 0.1, with a variable
step size reduced from coarse to fine near the global
minimum. The SVM with the optimum training parameters
was then used to carry out 10-fold cross-validation for all
molecules in the training data set, so as to provide an internal
estimate of prediction accuracy, before being retrained
against all of the training data in order to provide a prediction
for molecules in the test set. The optimum training param-
eters were epsilon ) 0.1, cost ) 3, and gamma ) 0.08.
An artificial neural network was trained by a systematic
search method so as to optimize the prediction for an internal
hold-out test set. Multilayer feed-forward backward-propaga-
tion neural networks were used, and both single and double
hidden layer architectures were investigated. The systematic
search was carried out in the following ranges: number of
neurons in each hidden layer, from 3 to 12; number of
iterations, from 50 to 300; learning rate, from 0.004 to 0.016;
and momentum, from 0.1 to 1.1. The search was repeated
for both a least-mean-squares (LMS) and a least-mean-
logarithm-squared (LMLS) error criterion. The best network
had a 12-6-1 architecture and was trained over 150 epochs
with a learning rate of 0.01, momentum of 0.5, and a LMLS
Diversity Analysis. QSPR’s are empirical models and as
such will be most successful in making predictions for
molecules which are similar to the training set. To investigate
the diversity of our solid-only data set, the MDDR was
selected as an example of the chemical space occupied by
druglike organic molecules.
Figure 1. Derivation of four different regression models.
Figure 2. Increase in mean-square-error when a single descriptor
in the Random Forest model is replaced by random noise; shown
for the top 40 descriptors as ranked in order of importance.
D J. Chem. Inf. Model.PALMER ET AL.
The MDDR contains some molecules that cannot be
processed by our software. Therfore, prior to the diversity
analysis, some standard filters were applied to the MDDR
so as to remove those entries that contained counterions, one
of each pair of duplicated molecules and all entries that
contained elements other than C, H, N, O, S, P, F, Cl, Br,
and I. The final MDDR data set contained 108 298 mol-
For these 108 298 molecules, the 2D descriptors selected
for the regression analysis (Table 1) were calculated. A
Principal Component Analysis (PCA) was carried out on all
scaled and centered descriptors, and the rotation matrix was
then used to predict the scores for the MDDR data set and
for the QSPR training and test data sets. The distributions
of the significant PC scores for the MDDR data set and the
QSPR training and test sets were compared. The method is
similar to laying down a map (the MDDR) and then locating
places (the molecules in the QSPR data sets) within it. In
this respect it is similar to the Chemography method of
Oprea.8A benefit of using the MDDR in conjunction with
diversity analysis is that if a region of chemical space is
identified which is not represented in the QSPR data sets,
then it is trivial to identify molecules from the MDDR which
do occupy this region.
The MDDR analysis illustrates the diversity of our data
set with respect to a larger region of chemical space. To
provide a clear example of the relative similarity between
structures in the training and test sets, a separate PCA was
carried out based upon the molecules in the training data
set. Figure 6 illustrates the factor scores for both training
and test data sets.
Random Forest Regression. The first Random Forest
model was trained upon all 126 2D descriptors, and the out-
of-bag cross-validation results were used to select the
optimum training parameters (mtry ) 42, ntree ) 500, and
nodesize ) 5). However, the optimum value of mtry will
depend on the total number of descriptors. Therefore it is
more easily expressed as the fraction of the total number of
descriptors (in this case, the total number of descriptors
divided by three). For the training set the Random Forest
was able to explain a large proportion of the variance in the
log molar solubility values giving r2(tr) ) 0.98, RMSE(tr)
) 0.28, and bias(tr) ) 0.007. The fit of the model to the
training data is not an estimate of the predictive ability of
the model, and so the out-of-bag validation results were
examined, for which r2(oob) ) 0.89, RMSE(oob) ) 0.69,
and bias(oob) ) 0.017. The 10-fold cross-validation results
were also calculated so as to provide a comparison with those
for PLS, SVM, and ANN models, the results were r2(CV)
) 0.896, RMSE(CV) ) 0.685, and bias(CV) ) 0.010.
The importance of each descriptor to the model was
assessed. Figure 2 is a plot of the descriptors ranked by their
importance (only the first 40 descriptors are shown). Figure
2 demonstrates that few of the descriptors contribute
significantly to the model. The Random Forest was iteratively
retrained, and each time the five least important descriptors
were removed. However, no improvement to the prediction
error could be made. For example, a Random Forest trained
upon the 40 most important descriptors gave a fit to the
training data set which was identical to that trained upon all
descriptors (r2(tr) ) 0.98, RMSE(tr) ) 0.29, and bias(tr) )
0.000 and r2(oob) ) 0.89, RMSE(oob) ) 0.695, and bias-
(oob) ) 0.005). In fact, there are a number of reasons why
it is unnecessary to remove irrelevant descriptors from RF
models. First, Random Forest contains a method for descrip-
tor selection. Second, neither the training of the Forest nor
the calculation of the 2D descriptors is computationally
expensive, and hence there is no need to define a subset in
order to reduce computing time. Third, due to intercorrela-
tions between descriptors the exact order of importance of
descriptors may change, and hence the subset selected by
our method may not be the unique solution. For this reason,
we select the Random Forest model for which all 2D
descriptors are available.
When Random Forest training was repeated 10 times, the
five most important descriptors were the same in each model
and were (in order of importance) SlogP, PEOE•VSA•NEG,
a•hyd, SMR, and vsa•hyd. The order of the following
descriptors varied slightly between each model; however,
the positions 6-12 were always occupied by a subset
of PEOE•VSA•FHYD, chi1v,
PEOE•VSA•FPNEG, Weight, TPSA, PEOE•RPC-1, and
Table 1. Twelve Descriptors Selected by the ACO/PLS Procedure
octanol-water partition coefficient
relative negative partial charge: the smallest
negative atomic partial charge
divided by the sum of the negative
atomic partial charges
fractional polar van der Waals surface area.
The sum of the van der Waals surface
area of atoms which have an absolute
partial charge greater than 0.2 divided
by the total surface area.
PEOE•VSA•FNEG fractional negative van der Waals surface area
TPSAtotal polar surface area
a•acc number of hydrogen bond acceptors
a•don number of hydrogen bond donors
weinerPolWeiner polarity index
a•aronumber of aromatic atoms
b•rotR fraction of rotatable bonds
chi1v•C carbon valence connectivity index (order 1)
Figure 3. The 12 most important descriptors for a particular
Random Forest, as measured by increase in MSE on replacement
with random noise. Slight permutations occur in the order of
descriptors below vsa•hyd when different Forests are trained with
the same data.
RANDOM FOREST MODELS
J. Chem. Inf. Model.E
chi0v, and the top 25 ranked descriptors always belonged to
a subset of 32 descriptors.
Partial-Least-Squares. The descriptors selected by the
ACO/PLS procedure are given in Table 1.
When the PLS model was trained upon the complete
training set, the results were as follows: r2(tr) ) 0.873,
RMSE(tr) ) 0.760, bias(tr) ) 0.000, r2(CV) ) 0.856,
RMSE(CV) ) 0.787, and bias(CV) ) 0.001.
Support Vector Machines. The descriptors selected by
the ACO/PLS procedure were used as input for an epsilon
regression Support Vector Machine with a radial basis
function kernel. The statistics for 10-fold cross-validation
inside the training set were r2(CV) ) 0.880, RMSE(CV) )
0.726, and bias(CV) ) 0.001. The SVM was trained based
upon 478 support vectors, and all descriptors were scaled
and centered prior to calculation.
Neural Networks. The best network had a 12-6-1
architecture and was trained over 150 epochs of adaptive
gradient descent with a least-mean-logarithm-squared error
criterion, a learning rate of 0.01, and a momentum of 0.5.
For 10-fold cross-validation inside the training set, r2(CV)
) 0.864, RMSE(CV) ) 0.742, bias(CV) ) -0.020.
External Validation. Table 2 contains the results for all
four different methods. Based upon the cross-validation
results for all four models, it can be established that the
Random Forest performs better than the SVM, ANN, and
PLS models (in order of increasing predictive error). The
same is true for the prediction of the external test set. Fig-
ure 4 shows the correlation between calculated and experi-
mental log solubility values for the RF model. The Random
Forest model was able to predict the log molar solubility
values for the molecules in the test set with r2(te) ) 0.89,
RMSE(te) ) 0.69, and bias(te) ) 0.05. The ability of the
Random Forest to predict values for molecules not contained
within the training set, in conjunction with the 10-fold and
out-of-bag cross-validation statistics, suggests that the model
is useful for the prediction of the aqueous solubility of as
yet unsynthesized molecules.
3D Descriptors. It was hoped that the inclusion of 3D
descriptors would further improve the model. Of particular
interest were the solvent-accessible-surface-area (SASA)
descriptors, which are expected to have a strong correlation
with the energy for cavity formation in the solvent (as
exemplified by the use of solvent-accessible-surface areas
for the empirical prediction of cavity energy in molecular
modeling programs32). The Random Forest was retrained
upon all 2D and 3D descriptors with the training parameters
of mtry ) 61, nodesize ) 5, and ntree ) 500. The
validation statistics were found to be similar to those for the
2D model. For the out-of-bag validation r2(oob) ) 0.89,
RMSE(oob) ) 0.694, and bias(oob) ) 0.01. When the
descriptor importance was assessed, it was found that few
3D descriptors were selected within the top 40 most
important descriptors. Although SASA was among the higher
ranked descriptors, the failure to improve the regression
model demonstrates that similar information is encoded in
the 2D descriptors. For this reason, the 2D model was
selected in preference to that which included 3D descriptors.
Comparison between Different Models. The Random
Forest and ACO selected models contain different subsets
of descriptors. The observation is not surprising given that
many of the descriptors from which the models are selected
have high pairwise correlations. There is therefore some
redundancy in the descriptor set and hence a variety of
solutions of similar merit.
Eight of the twelve descriptors selected by the ACO/PLS
procedure are also within the top 25 ranked descriptors in
the Random Forest model (SlogP, SMR, PEOE•RPC-1,
PEOE•VSA•FPOL, PEOE•VSA•FNEG, TPSA, a•acc,
and weinerPol), but the remaining four (a•don, a•aro,
b•rotR, and chi1v•C) do not contribute significantly to the
model. The corollary of this is that there are many descriptors
Weight, etc.) which appear to be important to Random Forest
but which were not selected by the ACO algorithm. The
observation that the subset of descriptors that are important
for Random Forest is larger than that for the other methods
(see Figure 2) is misleading. The predictive ability of
Random Forest is not affected by the presence of correlated
descriptors, and therefore none were removed prior to
Figure 4. Calculated versus experimental log molar solubility for the best Random Forest model for the training and test sets.
Figure 5. The first three principal component scores for the MDDR
data set (black), the training data set (blue), and the test data set
F J. Chem. Inf. Model.PALMER ET AL.
analysis. However, there will be an effect on the number of
descriptors that are assessed as being important. An artificial
example of this is provided if the most important descriptor
is duplicated in the data set. As the descriptors are identical
they will have (nearly) identical measures of importance and
will both occur in the list of important descriptors. The list
will therefore be one descriptor longer, but the predictive
ability does not change.
Diversity Analysis. The diversity of the sample selected
for the QSPR training (and test) data sets will affect how
well the model generalizes to unseen data. An ideal QSPR
data set would be an evenly distributed sample of the
population (organic chemical space).
The principal component scores for the MDDR data set
were compared to those predicted for the QSPR training and
test data sets. The scores derived from the six principal
components with the highest eigenvalues were considered;
these explain 92.5% of the variance in the MDDR data set.
The largest difference between the chemical space occupied
by the MDDR and that occupied by the training and test
sets is observed for the first PC score and is shown in Figure
5. There are a large number of molecules in the MDDR with
a PC1 score greater than 2.5 which are not represented by
the QSPR data set. Analysis of the MDDR revealed that there
are 12406 molecules in the MDDR which fall into this
region. However, there is a strong correlation between PC1
score and molecular size, and it is found that the mean
molecular weight for these molecules is 585.54. Molecules
from the QSPR data set that do occupy this region of
chemical space are norbormide (Figure 6, lower right-hand
side), a calcium channel entry blocker, which is used as a
rat poison, and etoposide, an antitumor agent which inhibits
the enzyme topoisomerase II. By contrast, some of the
molecules in the training and test data sets appear to be on
the fringes of the chemical space occupied by the MDDR.
Examples include glucose and fructose.
With the exceptions mentioned above, the data set is not
localized within a small volume of chemical space but is
structurally diverse. Furthermore, the training and test sets
occupy similar regions of chemical space; therefore, the
external validation is generally an interpolative prediction.
However it should be noted that the data set is not a perfect
sample of druglike space. In addition to some druglike
molecules the data set also contains some agrochemicals and
some nondruglike molecules.
Comparison with Other Studies. The direct comparison
of QSPR models that use different data sets is difficult as
the reported statistics will depend on the size, diversity, and
constitution of the test set. A comparison between our model
and others in the literature is further complicated by the fact
that it is not always clear which studies have used data sets
that contain molecules which are liquid at room temperature.
As an example, Lind et al. used a SVM equipped with a
Tanimoto kernel in order to predict the solubility of an
external test set of 412 molecules with r2(te) ) 0.89 and
RMSE(te) ) 0.68.10However this test data set was almost
identical to the test set of Huuskonen et al.25and is known
to contain both liquids and solids. Cheng et al. used
multilinear regression based upon 2D molecular descriptors
to derive a model which was able to predict log molar
solubility in the range of 0.7-1 log S units for four different
test sets.1However it is not possible to assess whether these
test sets contained molecules which were liquids at room
The prediction of solubility from structure has been the
subject of three recent reviews.11-13More than 90 different
models are discussed in these reviews, and no best model
can easily be identified. However, it is clear that for the
Figure 6. Factor score plot for molecules in the training set (black) and test set (red).
Table 2. Results for 10-Fold Cross-Validation Inside the Training Set and for Prediction of the External Test Set
model descriptor selectionr2(CV)RMSE (CV)bias(CV)r2(te) RMSE(te) bias(te)
RANDOM FOREST MODELS
J. Chem. Inf. Model.G
prediction of solubility for druglike molecules a minimum
standard error of prediction in the range of 0.5-1 log S unit
should be expected, which is comparable to that demonstrated
by the Random Forest model.
In the Introduction we made the assumption that the
molecular descriptors might be able to account for some of
the influence of the lattice energy on solubility during the
process of model building. In the same way, the descriptors
might account for the cohesive forces present in liquids
should these molecules be present in the training set.
Furthermore, we were interested in comparing our method
to those models which had employed mixed phase data sets.
For this reason the Huuskonen data set was selected from
the literature. The Random Forest model was retrained upon
the data set of Huuskonen et al. Table 3 provides a
comparison of the results of this study with six other methods
which have used the Huuskonen data set. Unfortunately since
each study used different permutations of training and test
sets, it was only possible to replicate exactly the data sets
selected by Liu et al.33(from which the results for QMPR+
are also taken). To approximate the data sets of Yan et al.,34
Tetko et al.,35Huuskonen et al., and Lind et al., a variant of
k-fold cross-validation was used. Model building was
repeated five times for randomly selected training and test
sets of the correct size, and the reported statistics are the
mean r2(te) and the mean standard deviation.
Solid or Liquid. The Huuskonen data set contains both
molecules that are liquid and molecules that are solid at room
temperature, and therefore it was interesting that the model
performed well in the tests shown in Table 3. To investigate
this further, the Huuskonen data set was partitioned into a
data set of 491 molecules which were known to be liquids
at room temperature and 744 molecules known to be solids.
Random Forest was used to analyze the data sets. For the
group of liquids the model was able to explain most of the
variance in the data set with r2(tr) ) 0.990, RMSE(tr) )
0.161, and bias(tr) ) 0.002. For out-of-bag validation,
r2(oob) ) 0.931, RMSE(oob) ) 0.405, and bias ) 0.005.
This was a better result than obtained for the solid only
portion of the Huuskonen data set, for which the best model
reported r2(tr) ) 0.985, RMSE(tr) ) 0.264, bias(tr) ) 0.000,
r2(oob) ) 0.910, RMSE(oob) ) 0.653, and bias ) 0.002.
Model Building. The QSPR methods for predicting
solubility which are documented in the literature can be
roughly divided into those which employ linear regression
methods (MLR, PLS, and PCR) and those which employ
machine learning methods (ANN and SVM). With one
notable exception,9the majority of studies that use machine
learning methods have reported better validation statistics
than those that use linear methods. In those papers, the most
commonly used machine learning method is the Artificial
Neural Network, which has been used in commercial as well
as academic models. Here we have demonstrated that
Random Forest regression has performed better than Neural
Networks for this data set. We have also developed a model
from a Support Vector Machine with a radial basis function
kernel which has proven to be useful for the prediction of
aqueous solubility. There are other reasons why Random
Forest might be of greater use than SVMs or ANNs. First,
Random Forest is easier to train as it includes a descriptor
selection procedure and is not strongly dependent upon
training parameters. Random Forests are immune to the
problems of overfitting common to ANNs and SVMs.
Second, descriptor importance can be assessed in Random
Forest, which aids in model interpretation.
The inclusion of 3D descriptors did not improve the
models. A possible explanation is that strong correlations
were observed between some 2D and 3D descriptors. The
Pearson correlation coefficient (r) between the 2D Molar
Refractivity and the 3D Water-Accessible-Surface Area was
0.95 and that between the 2D and 3D calculated VDW
volume was 1.00 (2 decimal places). However this does not
explain why 3D descriptors did not replace 2D descriptors
in some models. It is also possible that the use of a single
conformation to generate 3D descriptors limits their useful-
Solids or Liquids. The models developed in the literature
have often focused upon mixed-phase data sets which contain
many simple organic molecules. However, prediction of the
solubility of larger solid-phase molecules which contain
many functional groups is often of more interest; an example
would be the importance of solubility to the pharmaceutical
industry. Therefore we have focused upon developing models
for a large data set of molecules that are solid at room
temperature. However, we have also provided a comparison
with the literature for a data set which contains both solids
and liquids. Taking the RMSE(oob) as a guide of the
predictive accuracy of the models, the models prepared for
the liquid-only data set show higher predictive accuracy than
those for data sets containing solids. Some tentative conclu-
sions can be drawn from this observation. The simplest
conclusion would be that models that contain liquids in the
training and test sets will report better validation statistics;
an important point when comparing different QSPR models.
A survey of the literature would support this conclusion.
However we caution against concluding from these data that
the reason solids cannot be modeled as accurately as liquids
is that the molecular descriptors do not account for the lattice
energy of the crystal. The liquid only data set contains a
large number of simple organic molecules and is therefore
less diverse than the data set of solids. Furthermore, it is
reasonable to propose that experimental errors may be higher
for solubility measurements made for solid (crystalline)
materials which may exhibit polymorphism or which may
form solvates. In this work, the effect of the crystal form on
solubility was not considered explicitly, but rather it was
hoped that it would be accounted for indirectly during the
Table 3. Comparison between Our Random Forest Model and Five
Different Models from the Literature for a Data Set That Contains
Both Liquids and Solids
H J. Chem. Inf. Model.PALMER ET AL.
CONCLUSION Download full-text
A method for the prediction of aqueous solubility has been
developed from a structurally diverse data set based upon
2D molecular descriptors. The method has been shown to
perform comparably to other studies in the literature,
including some that require 3D structure calculation. Our
method is unique in that it uses Random Forest regression,
which we have shown performs better than models built by
Support Vector Machines or Artificial Neural Networks for
this data set. Furthermore, the results confirm that the
predictive statistics for solubility QSPR models will depend
on the phase adopted by the molecules in the data set at the
The solubility data were selected from the literature.
Although care was taken in excluding inconsistent data, the
experimental error will be similar to other studies which use
literature data. Clark et al.36have estimated this experimental
error to be in the region of 0.6 log S units. Therefore to
improve the models described here, new experimental data
are required. Work has begun in our laboratory to make new
measurements of solubility for druglike molecules. For each
molecule we are also measuring pKa’s and melting points
and characterizing the crystal structure by X-ray crystal-
We thank Pfizer for sponsoring this work through the
Pfizer Institute for Pharmaceutical Materials Science. We
acknowledge Unilever plc for their financial support of the
Centre for Molecular Science Informatics. N.M.O.B. was
supported by BBSRC grant BB/C51320X/1.
Supporting Information Available: Training and test
data sets. This material is available free of charge via the
Internet at http://pubs.acs.org.
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